With more than 90 million field hours and counting, Gridware is already detecting the small changes that lead to big problems.Together with utility companies, we’re preventing hazards from causing harm. We’re helping crews restore power quickly and safely.... Backed by Sequoia and Y Combinator.
About the role
We are seeking a Senior Applied Scientist, On-Device ML to design models that operate on multimodal time-series sensor data in highly resource-constrained environments. You will develop algorithms that balance accuracy with strict power and memory limits, helping advance the next generation of Gridware’s edge intelligence. This role blends applied research, model optimization, and low-level implementation in collaboration with hardware and firmware teams.
What they're looking for
- MS or PhD in Computer Science, Electrical Engineering, or a related technical field
- 3+ years of experience developing and deploying production ML models, on-device
- 3+ years of applied research experience in ML or algorithm development
- Hands-on experience working with physical sensors such as IMU, magnetometer, audio and modeling time-series data
- Strong foundation in ML architectures and on-device algorithm design for real-world systems
More about this role
About Gridware
Gridware is a San Francisco-based technology company dedicated to protecting and enhancing the electrical grid. We pioneered a groundbreaking new class of grid management called active grid response (AGR), focused on monitoring the electrical, physical, and environmental aspects of the grid that affect reliability and safety. Gridware’s advanced Active Grid Response platform uses high-precision sensors to detect potential issues early, enabling proactive maintenance and fault mitigation. This comprehensive approach helps improve safety, reduce outages, and ensure the grid operates efficiently. The company is backed by climate-tech and Silicon Valley investors. For more information, please visit www.Gridware.io .
We are seeking a Senior Applied Scientist, On-Device ML to design models that operate on multimodal time-series sensor data in highly resource-constrained environments. You will develop algorithms that balance accuracy with strict power and memory limits, helping advance the next generation of Gridware’s edge intelligence. This role blends applied research, model optimization, and low-level implementation in collaboration with hardware and firmware teams.
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